1. Field of the Invention
The present invention relates generally to the field of consumer credit scoring and credit risk prediction, and, more particularly, the present invention relates to the utilization of a novel income risk based credit scoring system using an individual's unemployment risk probability and income loss risk, and factoring the impact of economy on consumers' credit risk, to increase the accuracy of consumer credit risk forecasts resulting in credit loss reductions, increase in acquisitions, increase in portfolio credit quality, and an increase in profitability in the consumer credit industry.
2. Description of the Background
Individual borrowers pay their loans or loan installments when they have the ability to pay. The ability to pay largely depends on a person's disposable income. And if a person's disposable income disappears due to the loss of his job, or due to income reduction resulting from a pay cut or a change in job or due to underemployment, then the person assumes a much higher risk of defaulting on his loan repayments simply because the person has no money and therefore has no ability to pay. That is why it is critical to predict a person's ability to pay based on his future probability of loss of income or a reduction in income in order to make a superior prediction of his creditworthiness. Today, the standard approach to credit scoring is through traditional credit scores but the problem is that they are increasingly becoming inaccurate, simply because they don't predict future ability to pay. They are essentially reactive scores, meaning they change after borrowers default, and do not factor changes in the economy, and purely rely on credit histories and consumers' past ability to pay.
The problem this invention solves is that traditional credit bureau scores are not very accurate and have many significant limitations. Specifically, there are 3 problems with credit bureau scores. First problem is that credit bureau scores are reactive scores. The reason credit bureau scores are reactive is because they change only after the borrower defaults. The second problem is that credit bureau does not consider borrowers' income risk and that is why they can never be very accurate in predicting credit risk. The third problem with credit bureau scores is that they cannot score about 70 m people. This is because credit bureau scores can only be generated for people, who have long credit histories, but some 70 million people do not have credit histories or have very limited credit histories, and hence credit bureau model cannot score them. This means most lenders are not able to do business with these 70 million people.
To appreciate credit bureau score limitations let's take a look at credit bureau factors. The five key factors and their contribution to the overall credit bureau score are: payment history (35%), amount owed (30%), length of credit history (15%), types of credit (10%), and new credit (10%). As can be seen, credit bureau scores are entirely based on past credit behavior and does not factor future income risk or impact of economy on consumer's ability to pay. So, essentially credit bureau score is a measure of past credit risk and would work only for those people whose risk profile and income risk has not changed or been affected because of changes in the economy and business conditions.
A person is only able to repay a loan if the monetary sources are available which is usually dependent on consumer's continuance of present income and on consumer's intent to pay; thus, in effect the consumer's total credit is a function of both the willingness to pay and the “ability to pay.” Since an individual's ability to pay is directly related to continuance of income, defining that individual's credit risk using income loss risk and unemployment probability greatly increases the accuracy and effectiveness of credit risk prediction. Although, the consumer's income risk is a critical driver of credit risk it is not a factor in existing credit bureau scores.
The ability to pay is a critical factor in predicting credit risk, because a borrower must have both the willingness and the ability to repay a loan. If any one factor is missing then lenders will not get their payment. So the bottom line is that credit risk equals willingness to pay plus the ability to pay. And while it is useful to know the past willingness and ability, what really matters is the future willingness and future ability. And the future ability to pay depends on the borrower as well as the economic conditions, just as ‘accident risk’ depends on both the ‘driver’ and the ‘driving conditions’. Since ability to pay is such a critical driver of consumer creditworthiness, considering consumers' income risk and ability to pay in addition to the credit histories and payment histories will greatly enhance the predictive power of credit scoring models.
Consumer credit has traditionally been regarded to have three components: Collateral, Capacity, and Character (or Willingness). However, there is no collateral in cases of unsecured loans such as credit cards, capacity is equated with current income level, and willingness is judged based on past payment behavior. While credit bureau scores are based on the idea that a borrower's past payment behavior is indicative of their future payment behavior, a person's previous ability to pay is a less reliable predictor of future ability to pay compared to future continuance of income. Therefore existing credit scoring models fail to take into account consumer's true “capacity” to pay or ability to pay which depends on consumer's future continuance or income risk. But the present invention addresses this unmet need by providing a method to determine a consumer's income risk and the dependent credit risk.
As of September 2009, the Applicant is the only provider of income risk based credit score in the industry. No other invention has been able to so accurately calculate an unemployment probability and ability to pay and, more importantly, incorporate income risk into a credit scoring system to offer new, better credit risk insights resulting in effective and accurate consumer credit risk predictions.
One embodiment of the income risk based credit score is the Job Security Score which is generated by a novel credit scoring system complaint with the Equal Credit Opportunity Act's (ECOA) Regulation B. As defined in Regulation B, a “credit scoring system” is a system that evaluates an applicant's creditworthiness mechanically, based on key attributes of the applicant and aspects of the transaction. It determines, alone or in conjunction with an evaluation of additional information about the applicant, whether an applicant is deemed creditworthy. 12 C.F.R. §202.2(p)(1).
Also, the Job Security Score qualifies as “an empirically derived, demonstrably and statistically sound, credit scoring system” as defined by Reg B. The Regulation B states:                To qualify as an empirically derived, demonstrably and statistically sound, credit scoring system, the system must be—                    i. based on data that are derived from an empirical comparison of sample groups or the population of creditworthy and noncreditworthy applicants who applied for credit within a reasonable period of time;            ii. developed for the purpose of evaluating the creditworthiness of applicants with respect to the legitimate business interests of the creditor utilizing the system (including, but not limited to, minimizing bad debt losses and operating expenses in accordance with the creditor's business judgment);            iii. developed and validated using accepted statistical principles and methodology; and            iv. periodically revalidated by use of appropriate statistical principles and methodology and adjusted as necessary to maintain predictive ability.Id. The regulation goes on to state:                        A creditor may use an empirically derived, demonstrably and statistically sound, credit scoring system obtained from another person or obtain credit experience from which to develop such a system. Any such system must satisfy the criteria set forth in paragraph (p)(1)(i) through (iv) of this section; if the creditor is unable during the development process to validate the system based on its own credit experience in accordance with paragraph (p)(1) of this section, the system must be validated when sufficient credit experience becomes available.        
The current system predicts consumer creditworthiness by predicting an individual's income risk and by empirical comparison of income risk and credit experiences of a large population of creditworthy and non-creditworthy applicants or accounts. The key difference between traditional credit scores and current invention is that traditional credit scoring systems compare an applicant's credit profile to credit experiences of others whereas the current scoring system compares an applicant's income risk profile to credit experiences of others. Consumers who have more stable income outlook because they have more job security are likely to be more creditworthy, which is proven by the fact that unemployed individuals default on their payment obligations a lot more than employed individuals. The current invention uses an innovative approach of using consumers' income risk in predicting their credit risk and has created a credit scoring system through empirical comparison and analysis of income loss experiences and credit default experiences.
Current bureau scoring models only take into account previous consumer credit transactions when creating a credit score and do not attempt to factor a key driver of credit risk which is lack of sufficient income. Current credit bureau scoring models predominantly use payment history, amounts owed on account, length of credit history, new credit inquiries, and types of credit used, and do not use probability of income continuance. They have not yet developed a forecasting method capable of generating future income predictions of consumers, and therefore, have no way to analyze a consumer's ability to pay. In addition, existing credit scoring models are unable to score consumers with little-to-no credit history, leaving a wide gap in its current scoring capabilities.
Other companies have attempted to supplement the credit scoring bureaus, but none have succeeded to the level of the current invention. This is due to the fact that all are based on credit data and payment data. None include a forecast of future income risk or unemployment probability as a factor in consumer credit risk assessment. Thus, they are restricted in their ability to make accurate credit risk predictions.
The current invention is a novel income loss based credit scoring model that is different from all known credit scoring models, and was constructed based on the personal data, employment and unemployment histories, and financial stress experiences of individuals from a national sample between hundred thousand and one million people and credit behavior data from actual borrowers numbering between one million and fifteen million borrowers. It takes into account the impact of the changing economy on consumers' income risk and the dependent credit risk by considering: national and local macroeconomic attributes such as the gross domestic product, unemployment rates, retail sales, inflation, bankruptcies, foreclosures, money supply, and energy prices; and attributes that pertain to a group of individuals, such as type of employer and occupation; data for individuals, such as income, years at present job, and years at present residence; and by finding patterns and mathematical relationships between historical macroeconomic data and economic conditions and individuals and their historical income risk, ability to pay, and credit risk. The model uses various modeling techniques to predict the likelihood of unemployment and credit risk up to thirty-six months in advance. The income risk based credit score can be used alone or in conjunction with other scoring models, e.g. FICO, for functions such as deciding whether to grant or deny a credit, setting credit limits, or reviewing the performance of an existing account.
Traditional credit scores, such as FICO scores, are generated entirely from the credit bureau's files, but Job Security Score primarily uses consumer's loan application data to generate income loss risk and then to make a prediction of consumer's creditworthiness. Since, the income risk based credit score does not rely on credit histories it can score everyone including those consumers who have limited or no established credit histories. Currently in the U.S. there are 40 to 70 million consumers who do not have any credit histories or have very little credit histories which means that traditional credit bureau scores cannot be meaningfully computed for them. However, the income risk based credit score and one of its embodiments, the Job Security Score, is easily able to score all these consumers. This allows lenders to offer credit to “thin-file” and “no-file” applicants. For consumers with sufficiently long credit histories and meaningful credit bureau scores, the income risk based credit score can still be used in combination with FICO or credit scores to add new risk insights and to improve the accuracy and effectiveness of consumer payment default evaluations.
The total yearly consumer credit card losses in the U.S. amount to over 80 Billion dollars. Thus, there is a great need for more accuracy in consumer credit risk prediction. One embodiment of the income risk based credit score, the Job Security Score, improves risk prediction by up to 30%, where even a 5% reduction in credit losses will save the credit card industry $4.1 billion annually (See FIG. 9). The increased ability of lenders, businesses, and others to forecast the consumer's ability to pay and credit default risk will enhance profitability by reducing losses, improving acquisitions and marketing, and by early identification of high default risk consumers.